通过增强体渲染的表面感知能力，更精确地恢复三维几何形状。
Sensing Surface Patches in Volume Rendering for Inferring Signed Distance Functions
- 利用梯度与距离信息构建射线交点附近的表面小块
- 在表面块上施加多视角一致性等显式约束，提升细节还原
- 适合需要高精度几何重建的应用，如工业逆向建模
在众多三维计算机视觉任务中，从多视角RGB图像恢复三维几何结构至关重要。最新方法通过最小化体渲染中的渲染误差来推断表示为有符号距离场（SDF）的几何结构。然而，由于体渲染对表面的感知能力有限，仍难以显式施加表面约束以获取更多几何细节。为此，本文提出一种通过体渲染更精准感知表面的SDF推断方法。利用梯度与有符号距离，在射线估计的交点附近随机采样点，构建一个小的表面块。由此可显式在所感知的表面块上施加约束，如多视角照片一致性，或来自深度/法线先验的监督，均通过体渲染实现。我们在场景基准上进行了数值与视觉对比评估，结果表明该方法优于最新技术，验证了其有效性。
原文摘要 · Abstract (English)
It is vital to recover 3D geometry from multi-view RGB images in many 3D computer vision tasks. The latest methods infer the geometry represented as a signed distance field by minimizing the rendering error on the field through volume rendering. However, it is still challenging to explicitly impose constraints on surfaces for inferring more geometry details due to the limited ability of sensing surfaces in volume rendering. To resolve this problem, we introduce a method to infer signed distance functions (SDFs) with a better sense of surfaces through volume rendering. Using the gradients and signed distances, we establish a small surface patch centered at the estimated intersection along a ray by pulling points randomly sampled nearby. Hence, we are able to explicitly impose surface constraints on the sensed surface patch, such as multi-view photo consistency and supervision from depth or normal priors, through volume rendering. We evaluate our method by numerical and visual comparisons on scene benchmarks. Our superiority over the latest methods justifies our effectiveness.
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